Overview

Dataset statistics

Number of variables11
Number of observations1993574
Missing cells1801
Missing cells (%)< 0.1%
Duplicate rows12952
Duplicate rows (%)0.6%
Total size in memory167.3 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

Dataset has 12952 (0.6%) duplicate rowsDuplicates
fact_latitude is highly overall correlated with fact_temperatureHigh correlation
cmc_precipitations is highly overall correlated with gfs_cloudnessHigh correlation
gfs_cloudness is highly overall correlated with cmc_precipitationsHigh correlation
fact_temperature is highly overall correlated with fact_latitudeHigh correlation
gfs_clouds_sea is highly skewed (γ1 = 70.86471545)Skewed
cmc_precipitations has 1118308 (56.1%) zerosZeros
gfs_cloudness has 537307 (27.0%) zerosZeros
gfs_clouds_sea has 1770761 (88.8%) zerosZeros
fact_temperature has 36825 (1.8%) zerosZeros

Reproduction

Analysis started2023-05-04 13:40:57.393331
Analysis finished2023-05-04 13:43:55.340542
Duration2 minutes and 57.95 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fact_time
Real number (ℝ)

Distinct146866
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5435818 × 109
Minimum1.53576 × 109
Maximum1.5513947 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 MiB
2023-05-04T15:43:55.541527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.53576 × 109
5-th percentile1.5365397 × 109
Q11.5396694 × 109
median1.5435824 × 109
Q31.5474909 × 109
95-th percentile1.5506171 × 109
Maximum1.5513947 × 109
Range15634680
Interquartile range (IQR)7821540

Descriptive statistics

Standard deviation4515794
Coefficient of variation (CV)0.0029255294
Kurtosis-1.2005714
Mean1.5435818 × 109
Median Absolute Deviation (MAD)3911880
Skewness-0.0014602904
Sum3.0772446 × 1015
Variance2.0392395 × 1013
MonotonicityNot monotonic
2023-05-04T15:43:55.720746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1546322400 545
 
< 0.1%
1546257600 521
 
< 0.1%
1546354800 479
 
< 0.1%
1546236000 435
 
< 0.1%
1537012800 408
 
< 0.1%
1547380800 401
 
< 0.1%
1549519200 399
 
< 0.1%
1545134400 395
 
< 0.1%
1547121600 393
 
< 0.1%
1543557600 393
 
< 0.1%
Other values (146856) 1989205
99.8%
ValueCountFrequency (%)
1535760000 275
< 0.1%
1535760120 1
 
< 0.1%
1535760360 1
 
< 0.1%
1535760420 1
 
< 0.1%
1535760540 1
 
< 0.1%
1535760600 1
 
< 0.1%
1535760720 1
 
< 0.1%
1535760900 45
 
< 0.1%
1535761020 4
 
< 0.1%
1535761140 1
 
< 0.1%
ValueCountFrequency (%)
1551394680 6
 
< 0.1%
1551394620 1
 
< 0.1%
1551394560 26
< 0.1%
1551394500 7
 
< 0.1%
1551394440 8
 
< 0.1%
1551394380 45
< 0.1%
1551394320 1
 
< 0.1%
1551394260 8
 
< 0.1%
1551394200 11
 
< 0.1%
1551394140 1
 
< 0.1%

fact_latitude
Real number (ℝ)

Distinct5168
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.154619
Minimum-67.601667
Maximum70.933056
Zeros0
Zeros (%)0.0%
Negative189374
Negative (%)9.5%
Memory size15.2 MiB
2023-05-04T15:43:55.912623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-67.601667
5-th percentile-26.539801
Q130.069201
median35.66
Q341.532398
95-th percentile51.956902
Maximum70.933056
Range138.53472
Interquartile range (IQR)11.463197

Descriptive statistics

Standard deviation20.886554
Coefficient of variation (CV)0.67041596
Kurtosis4.0056837
Mean31.154619
Median Absolute Deviation (MAD)5.713101
Skewness-2.0641358
Sum62109038
Variance436.24813
MonotonicityNot monotonic
2023-05-04T15:43:56.095551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.117 1848
 
0.1%
32.438702 1525
 
0.1%
36.8 1498
 
0.1%
35.66 1398
 
0.1%
29.359501 1353
 
0.1%
34.717999 1344
 
0.1%
34.3 1227
 
0.1%
28.209 1213
 
0.1%
37.6 1145
 
0.1%
35.207699 1117
 
0.1%
Other values (5158) 1979906
99.3%
ValueCountFrequency (%)
-67.601667 103
 
< 0.1%
-66.663056 72
 
< 0.1%
-63.4 270
 
< 0.1%
-62.5 276
 
< 0.1%
-62.233333 263
 
< 0.1%
-60.733333 278
 
< 0.1%
-53.8 154
 
< 0.1%
-53.7777 902
< 0.1%
-53.2537 137
 
< 0.1%
-51.8228 228
 
< 0.1%
ValueCountFrequency (%)
70.933056 107
 
< 0.1%
69.604167 87
 
< 0.1%
69.6 455
< 0.1%
69.433296 566
< 0.1%
67.016667 360
< 0.1%
67.012222 269
 
< 0.1%
66.600098 419
< 0.1%
66.571503 800
< 0.1%
66.566667 1
 
< 0.1%
66.249603 397
< 0.1%

fact_longitude
Real number (ℝ)

Distinct7804
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-27.038522
Minimum-166.339
Maximum175.388
Zeros266
Zeros (%)< 0.1%
Negative1154267
Negative (%)57.9%
Memory size15.2 MiB
2023-05-04T15:43:56.292525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-166.339
5-th percentile-120.183
Q1-91.149597
median-17.2146
Q324.6192
95-th percentile118.1
Maximum175.388
Range341.727
Interquartile range (IQR)115.7688

Descriptive statistics

Standard deviation74.360181
Coefficient of variation (CV)-2.750157
Kurtosis-0.72652935
Mean-27.038522
Median Absolute Deviation (MAD)65.179199
Skewness0.49669234
Sum-53903294
Variance5529.4366
MonotonicityNot monotonic
2023-05-04T15:43:56.468995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.348007 1704
 
0.1%
-118.152 1330
 
0.1%
-0.45 1314
 
0.1%
-1.45 1303
 
0.1%
121.461998 1302
 
0.1%
-81.688904 1257
 
0.1%
-98.956497 1143
 
0.1%
-90.933 1107
 
0.1%
-122.655998 1104
 
0.1%
-83.561694 1089
 
0.1%
Other values (7794) 1980921
99.4%
ValueCountFrequency (%)
-166.339005 269
 
< 0.1%
-166.089005 397
< 0.1%
-165.607567 983
< 0.1%
-163.682007 725
< 0.1%
-163.302002 475
< 0.1%
-162.026001 470
< 0.1%
-161.319458 456
< 0.1%
-160.190994 123
 
< 0.1%
-159.985992 419
< 0.1%
-157.572006 385
 
< 0.1%
ValueCountFrequency (%)
175.388 2
 
< 0.1%
174.804993 936
< 0.1%
174.804993 62
 
< 0.1%
174.630005 1
 
< 0.1%
172.531998 987
< 0.1%
166.222222 64
 
< 0.1%
166.212997 876
< 0.1%
166.212997 39
 
< 0.1%
153.129167 102
 
< 0.1%
153.117004 964
< 0.1%

topography_bathymetry
Real number (ℝ)

Distinct1449
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.57889
Minimum-2126
Maximum4659
Zeros18358
Zeros (%)0.9%
Negative111562
Negative (%)5.6%
Memory size15.2 MiB
2023-05-04T15:43:56.660825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2126
5-th percentile-1
Q123
median116
Q3391
95-th percentile1487
Maximum4659
Range6785
Interquartile range (IQR)368

Descriptive statistics

Standard deviation519.41096
Coefficient of variation (CV)1.5807801
Kurtosis6.9017953
Mean328.57889
Median Absolute Deviation (MAD)109
Skewness2.2257525
Sum6.5504634 × 108
Variance269787.75
MonotonicityNot monotonic
2023-05-04T15:43:56.841022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 33653
 
1.7%
8 26611
 
1.3%
5 25841
 
1.3%
2 25804
 
1.3%
3 23444
 
1.2%
4 22204
 
1.1%
7 21038
 
1.1%
10 20244
 
1.0%
9 18680
 
0.9%
0 18358
 
0.9%
Other values (1439) 1757697
88.2%
ValueCountFrequency (%)
-2126 265
 
< 0.1%
-1933 71
 
< 0.1%
-1930 680
< 0.1%
-1753 226
 
< 0.1%
-1489 608
< 0.1%
-1425 539
< 0.1%
-1057 151
 
< 0.1%
-1055 593
< 0.1%
-1016 493
< 0.1%
-970 798
< 0.1%
ValueCountFrequency (%)
4659 131
 
< 0.1%
4364 11
 
< 0.1%
4111 108
 
< 0.1%
4048 700
< 0.1%
3965 215
 
< 0.1%
3832 452
< 0.1%
3717 295
< 0.1%
3655 98
 
< 0.1%
3579 322
< 0.1%
3446 188
 
< 0.1%

sun_elevation
Real number (ℝ)

Distinct1793421
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.8602098
Minimum-89.89978
Maximum89.865169
Zeros0
Zeros (%)0.0%
Negative1043744
Negative (%)52.4%
Memory size15.2 MiB
2023-05-04T15:43:57.032021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89.89978
5-th percentile-65.79618
Q1-36.675595
median-2.9114505
Q324.224601
95-th percentile48.700598
Maximum89.865169
Range179.76495
Interquartile range (IQR)60.900195

Descriptive statistics

Standard deviation36.724977
Coefficient of variation (CV)-6.2668366
Kurtosis-0.9523324
Mean-5.8602098
Median Absolute Deviation (MAD)29.836397
Skewness-0.095283224
Sum-11682762
Variance1348.7239
MonotonicityNot monotonic
2023-05-04T15:43:57.217678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.78954716 6
 
< 0.1%
-51.39815877 5
 
< 0.1%
-44.26653644 5
 
< 0.1%
37.36450375 5
 
< 0.1%
30.23352089 5
 
< 0.1%
-44.84953997 5
 
< 0.1%
-22.67197834 5
 
< 0.1%
18.25191251 5
 
< 0.1%
23.45690836 5
 
< 0.1%
0.06783851886 5
 
< 0.1%
Other values (1793411) 1993523
> 99.9%
ValueCountFrequency (%)
-89.89977966 1
< 0.1%
-89.82985219 1
< 0.1%
-89.8288449 1
< 0.1%
-89.79575421 1
< 0.1%
-89.77175682 1
< 0.1%
-89.66601115 1
< 0.1%
-89.64065028 1
< 0.1%
-89.60511773 1
< 0.1%
-89.59565779 1
< 0.1%
-89.54558173 1
< 0.1%
ValueCountFrequency (%)
89.86516883 1
< 0.1%
89.7490959 1
< 0.1%
89.74232221 1
< 0.1%
89.71892931 1
< 0.1%
89.71235037 2
< 0.1%
89.66067586 1
< 0.1%
89.63101691 1
< 0.1%
89.61384268 1
< 0.1%
89.56475528 1
< 0.1%
89.55908458 1
< 0.1%

cmc_precipitations
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct303375
Distinct (%)15.2%
Missing1801
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.13825675
Minimum-3.3333333 × 10-5
Maximum24.400417
Zeros1118308
Zeros (%)56.1%
Negative23422
Negative (%)1.2%
Memory size15.2 MiB
2023-05-04T15:43:57.412506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3333333 × 10-5
5-th percentile0
Q10
median0
Q30.017566667
95-th percentile0.79446667
Maximum24.400417
Range24.40045
Interquartile range (IQR)0.017566667

Descriptive statistics

Standard deviation0.54766782
Coefficient of variation (CV)3.9612375
Kurtosis136.69358
Mean0.13825675
Median Absolute Deviation (MAD)0
Skewness8.9741358
Sum275376.06
Variance0.29994005
MonotonicityNot monotonic
2023-05-04T15:43:57.584541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1118308
56.1%
8.333333333 × 10-64161
 
0.2%
1.666666667 × 10-53238
 
0.2%
-8.333333333 × 10-63092
 
0.2%
8.333333333 × 10-62661
 
0.1%
-8.333333333 × 10-62472
 
0.1%
1.666666667 × 10-52203
 
0.1%
8.333333334 × 10-62133
 
0.1%
3.333333333 × 10-51828
 
0.1%
8.333333333 × 10-61612
 
0.1%
Other values (303365) 850065
42.6%
(Missing) 1801
 
0.1%
ValueCountFrequency (%)
-3.333333333 × 10-59
< 0.1%
-3.333333333 × 10-52
 
< 0.1%
-3.333333333 × 10-52
 
< 0.1%
-3.333333333 × 10-59
< 0.1%
-3.333333333 × 10-52
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-52
 
< 0.1%
-3.333333333 × 10-55
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-52
 
< 0.1%
ValueCountFrequency (%)
24.40041667 1
< 0.1%
23.64301667 1
< 0.1%
22.65773333 1
< 0.1%
22.486875 1
< 0.1%
21.8554 1
< 0.1%
21.54189167 1
< 0.1%
20.6959 2
< 0.1%
20.4718 1
< 0.1%
20.45935 1
< 0.1%
20.3468 1
< 0.1%

gfs_a_vorticity
Real number (ℝ)

Distinct1197555
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2136137 × 10-5
Minimum-0.0007590648
Maximum0.0014602451
Zeros0
Zeros (%)0.0%
Negative271483
Negative (%)13.6%
Memory size15.2 MiB
2023-05-04T15:43:57.767760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0007590648
5-th percentile-7.7994819 × 10-5
Q14.3467056 × 10-5
median7.6943175 × 10-5
Q30.00011112744
95-th percentile0.00019493731
Maximum0.0014602451
Range0.0022193099
Interquartile range (IQR)6.7660384 × 10-5

Descriptive statistics

Standard deviation8.4585864 × 10-5
Coefficient of variation (CV)1.1725866
Kurtosis4.7632181
Mean7.2136137 × 10-5
Median Absolute Deviation (MAD)3.3844666 × 10-5
Skewness-0.48091844
Sum143.80873
Variance7.1547685 × 10-9
MonotonicityNot monotonic
2023-05-04T15:43:57.945735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.127502613 × 10-537
 
< 0.1%
8.127502224 × 10-529
 
< 0.1%
6.083739936 × 10-528
 
< 0.1%
7.083739911 × 10-527
 
< 0.1%
7.050354179 × 10-524
 
< 0.1%
6.804565055 × 10-524
 
< 0.1%
6.594192382 × 10-524
 
< 0.1%
7.594191993 × 10-523
 
< 0.1%
8.325470117 × 10-523
 
< 0.1%
7.263854786 × 10-523
 
< 0.1%
Other values (1197545) 1993312
> 99.9%
ValueCountFrequency (%)
-0.000759064802 1
< 0.1%
-0.0007015671581 1
< 0.1%
-0.0006876469706 1
< 0.1%
-0.0006734181079 1
< 0.1%
-0.0006620693603 1
< 0.1%
-0.0006607309333 1
< 0.1%
-0.0006553608109 1
< 0.1%
-0.0006519843591 1
< 0.1%
-0.000651513401 1
< 0.1%
-0.000650328584 1
< 0.1%
ValueCountFrequency (%)
0.001460245112 1
< 0.1%
0.001153926249 1
< 0.1%
0.001029804698 1
< 0.1%
0.001006142236 1
< 0.1%
0.0009886106709 1
< 0.1%
0.0009296169155 1
< 0.1%
0.0009029353969 1
< 0.1%
0.0008940886473 1
< 0.1%
0.0008887405857 1
< 0.1%
0.0008673816919 1
< 0.1%

gfs_cloudness
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct301
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72880642
Minimum0
Maximum3
Zeros537307
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size15.2 MiB
2023-05-04T15:43:58.147532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.49
Q31.11
95-th percentile2.42
Maximum3
Range3
Interquartile range (IQR)1.11

Descriptive statistics

Standard deviation0.80074271
Coefficient of variation (CV)1.0987043
Kurtosis0.19594162
Mean0.72880642
Median Absolute Deviation (MAD)0.49
Skewness1.0362948
Sum1452929.5
Variance0.64118889
MonotonicityNot monotonic
2023-05-04T15:43:58.336524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 537307
27.0%
1 60109
 
3.0%
0.01 48512
 
2.4%
0.02 26606
 
1.3%
0.03 19454
 
1.0%
0.99 18772
 
0.9%
2 16026
 
0.8%
0.04 15861
 
0.8%
0.05 13924
 
0.7%
0.98 13584
 
0.7%
Other values (291) 1223419
61.4%
ValueCountFrequency (%)
0 537307
27.0%
0.01 48512
 
2.4%
0.02 26606
 
1.3%
0.03 19454
 
1.0%
0.04 15861
 
0.8%
0.05 13924
 
0.7%
0.06 12436
 
0.6%
0.07 11062
 
0.6%
0.08 10397
 
0.5%
0.09 9733
 
0.5%
ValueCountFrequency (%)
3 12287
0.6%
2.99 3759
 
0.2%
2.98 2786
 
0.1%
2.97 2314
 
0.1%
2.96 2082
 
0.1%
2.95 1794
 
0.1%
2.94 1751
 
0.1%
2.93 1637
 
0.1%
2.92 1529
 
0.1%
2.91 1522
 
0.1%

gfs_clouds_sea
Real number (ℝ)

SKEWED  ZEROS 

Distinct1083
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5336503 × 10-7
Minimum0
Maximum0.00050880003
Zeros1770761
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size15.2 MiB
2023-05-04T15:43:58.528473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2 × 10-7
Maximum0.00050880003
Range0.00050880003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8769809 × 10-6
Coefficient of variation (CV)18.759041
Kurtosis7678.7795
Mean1.5336503 × 10-7
Median Absolute Deviation (MAD)0
Skewness70.864715
Sum0.30574454
Variance8.2770191 × 10-12
MonotonicityNot monotonic
2023-05-04T15:43:58.713529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1770761
88.8%
1.000000012 × 10-798512
 
4.9%
2.000000023 × 10-731794
 
1.6%
3.000000106 × 10-717382
 
0.9%
4.000000047 × 10-711626
 
0.6%
4.999999987 × 10-78584
 
0.4%
6.000000212 × 10-76520
 
0.3%
6.999999869 × 10-75100
 
0.3%
8.000000093 × 10-74165
 
0.2%
9.000000318 × 10-73405
 
0.2%
Other values (1073) 35725
 
1.8%
ValueCountFrequency (%)
0 1770761
88.8%
9.999999939 × 10-965
 
< 0.1%
1.999999988 × 10-824
 
< 0.1%
2.999999893 × 10-815
 
< 0.1%
3.999999976 × 10-85
 
< 0.1%
5.000000058 × 10-81
 
< 0.1%
5.999999786 × 10-84
 
< 0.1%
6.999999869 × 10-81
 
< 0.1%
7.999999951 × 10-82
 
< 0.1%
9.000000034 × 10-83
 
< 0.1%
ValueCountFrequency (%)
0.0005088000325 1
< 0.1%
0.0004847999953 1
< 0.1%
0.0004735999973 1
< 0.1%
0.0004704000021 1
< 0.1%
0.0004666000023 1
< 0.1%
0.000460500014 1
< 0.1%
0.0004472000001 1
< 0.1%
0.0004333000106 1
< 0.1%
0.0004297000123 1
< 0.1%
0.0004130000016 1
< 0.1%

gfs_humidity
Real number (ℝ)

Distinct278990
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.384893
Minimum1
Maximum100.04999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 MiB
2023-05-04T15:43:58.901537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.9
Q150.700001
median69
Q383.300003
95-th percentile95.099998
Maximum100.04999
Range99.049988
Interquartile range (IQR)32.600002

Descriptive statistics

Standard deviation22.123647
Coefficient of variation (CV)0.33836023
Kurtosis-0.47597763
Mean65.384893
Median Absolute Deviation (MAD)15.809517
Skewness-0.58102708
Sum1.3034962 × 108
Variance489.45576
MonotonicityNot monotonic
2023-05-04T15:43:59.076572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.90000153 3140
 
0.2%
78.5 3115
 
0.2%
77.20000458 3104
 
0.2%
78.09999847 3093
 
0.2%
77 3084
 
0.2%
79.70000458 3080
 
0.2%
74 3070
 
0.2%
76.80000305 3063
 
0.2%
76.09999847 3062
 
0.2%
75.70000458 3060
 
0.2%
Other values (278980) 1962703
98.5%
ValueCountFrequency (%)
1 5
< 0.1%
1.200000048 5
< 0.1%
1.300000072 1
 
< 0.1%
1.399999976 2
 
< 0.1%
1.600000024 3
 
< 0.1%
1.700000048 4
< 0.1%
1.800000072 1
 
< 0.1%
1.899999976 4
< 0.1%
2 9
< 0.1%
2.100000143 3
 
< 0.1%
ValueCountFrequency (%)
100.0499878 1
< 0.1%
100.0498123 1
< 0.1%
100.0496292 1
< 0.1%
100.0490036 1
< 0.1%
100.0488892 2
< 0.1%
100.0479889 1
< 0.1%
100.0469894 1
< 0.1%
100.0462036 1
< 0.1%
100.0450974 2
< 0.1%
100.0449982 1
< 0.1%

fact_temperature
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.317229
Minimum-45
Maximum60
Zeros36825
Zeros (%)1.8%
Negative146737
Negative (%)7.4%
Memory size15.2 MiB
2023-05-04T15:43:59.268822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile-2
Q16
median13
Q321
95-th percentile29
Maximum60
Range105
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.009715
Coefficient of variation (CV)0.75163647
Kurtosis0.01218938
Mean13.317229
Median Absolute Deviation (MAD)7
Skewness-0.11996873
Sum26548881
Variance100.19439
MonotonicityNot monotonic
2023-05-04T15:43:59.450422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 77108
 
3.9%
12 76023
 
3.8%
9 75728
 
3.8%
11 75604
 
3.8%
7 74496
 
3.7%
13 73524
 
3.7%
10 71742
 
3.6%
14 69489
 
3.5%
6 68690
 
3.4%
16 66354
 
3.3%
Other values (86) 1264816
63.4%
ValueCountFrequency (%)
-45 3
 
< 0.1%
-44 5
 
< 0.1%
-43 4
 
< 0.1%
-42 20
< 0.1%
-41 11
 
< 0.1%
-40 19
< 0.1%
-39 18
< 0.1%
-38 27
< 0.1%
-37 28
< 0.1%
-36 30
< 0.1%
ValueCountFrequency (%)
60 1
 
< 0.1%
49 2
 
< 0.1%
48 6
 
< 0.1%
47 25
 
< 0.1%
46 60
 
< 0.1%
45 118
 
< 0.1%
44 246
 
< 0.1%
43 363
< 0.1%
42 553
< 0.1%
41 786
< 0.1%

Interactions

2023-05-04T15:43:38.633455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:41:55.442946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:06.295087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:16.830636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:27.234564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:37.627550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:48.199584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:58.087504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:08.341958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:18.447503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:27.811265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:39.660507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:41:56.450795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:07.227778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:17.771669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:28.198272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:38.587383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:49.132494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:59.070382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:09.274635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:19.274816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:28.772168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:40.755444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:41:57.657441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:08.204069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:18.675425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:29.086380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:39.597323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:50.036131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:00.004217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:10.206653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:20.432019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:29.758472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:41.751449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:41:58.638709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:09.197368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:19.653321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:29.999416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:40.706322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:50.929926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:00.958645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:11.130653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:21.246686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:30.703558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:43.133835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:41:59.593580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:10.153587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:20.607673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:30.963689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:41.634401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:51.807527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:01.890435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:12.076603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:22.049567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:31.651515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:44.156397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:00.568734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:11.092867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:21.553679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:31.988626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:42.601308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:52.632731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:02.840193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:13.025358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:22.862296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:32.603883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:45.117219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:01.519218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:12.020461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:22.498105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:32.909048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:43.544281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:53.519398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:03.709645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:13.945096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:23.675134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:33.562215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:46.110516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:02.526892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:12.979618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:23.469540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:33.889378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:44.515096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:54.451780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:04.631457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:14.881299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:24.533424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:34.536662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:47.051523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:03.469357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:13.921367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:24.378114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:34.805330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:45.412459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:55.335427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:05.527517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:15.743493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:25.311744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:35.497018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:48.030457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:04.434461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:14.936530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:25.388104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:35.747191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:46.347659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:56.253622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:06.477278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:16.682542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:26.099497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:36.772996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:48.931624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:05.390602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:15.883136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:26.316349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:36.675464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:47.277580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:42:57.159083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:07.406175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:17.618331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:26.850512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:43:37.699467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-04T15:43:59.618640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fact_timefact_latitudefact_longitudetopography_bathymetrysun_elevationcmc_precipitationsgfs_a_vorticitygfs_cloudnessgfs_clouds_seagfs_humidityfact_temperature
fact_time1.0000.001-0.0030.002-0.0830.075-0.0060.1180.0380.058-0.453
fact_latitude0.0011.000-0.074-0.046-0.1460.1310.4780.1360.0840.212-0.531
fact_longitude-0.003-0.0741.000-0.1100.073-0.021-0.106-0.0720.063-0.1350.149
topography_bathymetry0.002-0.046-0.1101.0000.019-0.052-0.023-0.046-0.253-0.212-0.163
sun_elevation-0.083-0.1460.0730.0191.000-0.024-0.104-0.030-0.049-0.2450.356
cmc_precipitations0.0750.131-0.021-0.052-0.0241.0000.0530.5080.1660.482-0.115
gfs_a_vorticity-0.0060.478-0.106-0.023-0.1040.0531.0000.0520.0330.101-0.254
gfs_cloudness0.1180.136-0.072-0.046-0.0300.5080.0521.0000.1210.430-0.140
gfs_clouds_sea0.0380.0840.063-0.253-0.0490.1660.0330.1211.0000.195-0.021
gfs_humidity0.0580.212-0.135-0.212-0.2450.4820.1010.4300.1951.000-0.222
fact_temperature-0.453-0.5310.149-0.1630.356-0.115-0.254-0.140-0.021-0.2221.000

Missing values

2023-05-04T15:43:49.191672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-04T15:43:50.479642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fact_timefact_latitudefact_longitudetopography_bathymetrysun_elevationcmc_precipitationsgfs_a_vorticitygfs_cloudnessgfs_clouds_seagfs_humidityfact_temperature
01.538665e+0942.69669323.411436532.011.4361090.0000000.0001250.000.028.50000019.0
11.539436e+0951.4477784.34194420.026.9564210.0000000.0000950.740.062.84095425.0
21.541235e+0939.175400-76.66829742.0-45.9285530.2073920.0000682.800.070.70000511.0
31.544501e+0936.029598-119.063004132.0-25.8716000.0535500.0001661.280.078.4000029.0
41.538634e+0934.398300-96.148102179.0-61.0205010.0000000.0000790.170.086.70000523.0
51.546056e+0933.988800-98.491898302.0-42.3166090.0000000.0000740.000.062.7000011.0
61.545142e+09-17.87820115.9526001099.046.8204910.000000-0.0000350.990.019.10000033.0
71.536496e+0951.275799-0.77633374.042.4891760.0137080.0000870.990.071.40000221.0
81.541854e+0938.065498-97.860603470.0-17.1576280.0000000.0000640.000.054.299999-8.0
91.544880e+0932.239300-90.92839840.0-1.3495220.1139750.0000681.000.092.09999810.0
fact_timefact_latitudefact_longitudetopography_bathymetrysun_elevationcmc_precipitationsgfs_a_vorticitygfs_cloudnessgfs_clouds_seagfs_humidityfact_temperature
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Duplicate rows

Most frequently occurring

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